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Research Article
This protocol describes a standardized cross-sectional approach for measuring environmental noise across hospital functional zones. Application in a tertiary hospital identified nurses’ stations as the noisiest locations, with higher noise levels associated with visitor count and departmental bed capacity.
Hospital environments comprise complex acoustic settings generated by routine clinical activities, medical equipment, staff communication, patient movement, and visitor traffic. Characterizing environmental noise across different hospital functional zones may help identify areas with consistently elevated acoustic exposure and inform targeted noise-management strategies. We hypothesized that environmental noise would differ among hospital functional zones and be associated with selected operational characteristics. A cross-sectional observational study was conducted across 15 inpatient departments of a tertiary teaching hospital using a standardized hierarchical sampling framework. Environmental noise was measured within four predefined functional zones (ward, corridor, nurses’ station, and patient activity area) using a calibrated Class 1 integrating sound level meter. Equivalent continuous A-weighted sound pressure level (LAeq) measurements were obtained from repeated daytime recordings (09:00–19:00), together with maximum A-weighted sound pressure level (LAFmax) and peak C-weighted sound pressure level (LCpeak). Linear mixed-effects models were used to compare environmental noise among functional zones while accounting for repeated observations nested within hospital departments. Exploratory mixed-effects regression evaluated associations between environmental noise and selected operational characteristics. A total of 1,620 acoustic observations were analyzed. Nurses’ stations demonstrated the highest adjusted environmental noise levels (estimated marginal mean, 66.90 dBA), followed by corridors (63.97 dBA), patient activity areas (61.38 dBA), and wards (60.38 dBA). All pairwise comparisons remained statistically significant following Tukey adjustment (all P < 0.001). Visitor count and departmental bed capacity were independently associated with higher LAeq in the parsimonious mixed-effects model. Environmental noise levels consistently exceeded commonly referenced World Health Organization, EN ISO 16032, and GB3096-2022 reference acoustic values during routine daytime operations. Environmental noise varied significantly across hospital functional zones, with nurses’ stations representing the principal areas of elevated acoustic exposure. These findings support targeted acoustic monitoring and operational noise-management strategies while providing a reproducible framework for future hospital environmental acoustic research.
Hospital environments are acoustically complex settings in which sound is continuously generated by medical equipment, physiological monitoring systems, staff communication, patient movement, alarms, ventilation systems, and routine clinical activities. These diverse sound sources create dynamic healthcare soundscapes that vary across time and location within hospitals and may influence both patient experience and staff working conditions. Persistent or excessive environmental noise has been associated with sleep disturbance, impaired communication, increased cognitive workload, physiological stress responses, and reduced environmental comfort in healthcare settings1,2,3,4,5,6,7. Consequently, hospital noise has been recognized as an important environmental factor that may be amenable to targeted monitoring and mitigation strategies.
International organizations have proposed recommendations for maintaining relatively low sound levels in patient-care environments. The World Health Organization (WHO) recommends background sound levels of approximately 35 dB(A) during the daytime and 30 dB(A) at night in hospital patient rooms, whereas other national and international standards provide guidance for building acoustics, environmental noise assessment, or healthcare facility design. Because these recommendations differ in their intended purpose, measurement metrics, averaging periods, and scope, comparisons with operational hospital measurements should be interpreted cautiously. Nevertheless, environmental surveys of hospitals worldwide have consistently reported daytime equivalent sound levels that substantially exceed recommended background values, frequently ranging from 55 to 75 dB(A) in inpatient wards, corridors, emergency departments, and intensive care units8,9,10,11.
Accumulating evidence suggests that elevated hospital noise may adversely affect both patients and healthcare personnel. Among patients, excessive environmental noise has been associated with sleep disruption, reduced restfulness, physiological stress responses, diminished patient satisfaction, and, in critically ill populations, an increased risk of neurocognitive disturbances such as delirium12,13,14,15,16,17,18. For healthcare professionals, elevated background noise may reduce speech intelligibility, increase cognitive workload, interfere with clinical communication, and contribute to alarm fatigue and occupational stress, particularly in high-intensity clinical environments19,20,21,22,23. Although these observational findings do not establish causality, they underscore the importance of understanding the spatial distribution and operational characteristics associated with hospital acoustic environments.
Despite increasing interest in healthcare acoustics, many previous investigations have focused on individual wards, intensive care units, or hospital-wide average sound levels. Such approaches provide limited information regarding spatial heterogeneity within hospitals and often rely solely on time-averaged acoustic metrics obtained from a limited number of monitoring locations. More recent studies have emphasized that hospital soundscapes comprise a mixture of continuous background noise and transient impulsive events and have advocated more spatially resolved monitoring strategies capable of identifying operational hotspots and distinguishing among different sources of acoustic exposure. Contemporary approaches incorporating clustering analysis, sound-source characterization, and soundscape assessment have further highlighted the importance of evaluating environmental noise within its functional and operational context24,25,26,27,28.
To address these gaps, the present study performed a comprehensive environmental acoustic assessment across 15 inpatient departments within a tertiary teaching hospital. Environmental noise was systematically measured across four standardized functional zones—departmental wards, ward corridors, nurses’ stations, and patient activity areas—using repeated daytime measurements obtained under routine clinical operating conditions. In addition to characterizing spatial variability in equivalent continuous A-weighted sound pressure level (LAeq), the study explored associations between environmental noise and selected operational characteristics of hospital departments. We hypothesized that environmental noise exposure would differ significantly among hospital functional zones and that operational characteristics related to departmental activity would be associated with higher environmental sound levels. By providing a detailed spatial characterization of hospital acoustic environments, this study aims to identify priority areas for environmental monitoring and inform future evidence-based noise-management strategies within healthcare facilities.
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The study was approved by the local institutional ethics committee (Approval No. HEBMU-AUD/2025/017) and was conducted in accordance with the ethical principles of the 1964 Declaration of Helsinki and its subsequent amendments or comparable ethical standards. This study involved environmental acoustic monitoring only and did not include direct patient participation, clinical intervention, collection of biological samples, or acquisition of identifiable personal information. Because measurements were conducted exclusively at the environmental level under routine hospital operating conditions, the requirement for written informed consent was waived. Hospital personnel were informed of the observational nature of the study, and all collected data were anonymized and handled in accordance with institutional data-protection policies.
Study Design
This study was conducted as a cross-sectional observational assessment of environmental noise within a tertiary hospital setting. The primary objective was to measure and compare LAeq across fifteen inpatient departments and four standardized hospital functional zones to characterize the spatial distribution and variability of acoustic exposure within the healthcare environment. Maximum A-weighted sound pressure level (LAFmax) and peak C-weighted sound pressure level (LCpeak) were additionally recorded to characterize transient and impulsive acoustic events occurring during routine hospital operations. Measured sound levels were interpreted in relation to commonly referenced international and regional acoustic guidance documents and standards, including the WHO Community Noise Guidelines and Environmental Noise Guidelines, EN ISO 16032, PN-B-02151, and the Chinese National Standard GB3096-2022, where applicable. Because these documents differ in their intended purpose, measurement metrics, averaging periods, environmental settings, and scope, comparisons were interpreted descriptively rather than as formal compliance assessments.
The study focused on practical environmental acoustic assessment under routine hospital operating conditions rather than formal architectural acoustic certification testing. The investigation was designed to identify spatial variability in hospital noise exposure and to explore associations between environmental noise and objectively recorded operational characteristics, including departmental bed capacity, occupied beds, staff count, visitor count, alarm events, conversation events, room area, and room volume. Bed capacity, room area, and room volume were obtained from departmental administrative and engineering records before data collection. Occupied beds were recorded immediately before each acoustic measurement. During each 10-min acoustic recording, a trained observer simultaneously documented the number of staff members and visitors present within the predefined functional zone using direct visual counting. Alarm events were recorded as the total number of audible medical equipment alarm activations occurring during the same 10-min observation period, whereas conversation events were recorded as the total number of distinct conversational episodes involving patients, visitors, or healthcare personnel that were clearly audible within the measurement zone. Operational variables were recorded contemporaneously with each acoustic measurement using standardized case-report forms and identical counting procedures across all departments and functional zones. Observer training was completed before study initiation using standardized written instructions to ensure consistent implementation of the recording protocol. These operational variables were collected as objective descriptors of routine clinical activity and were analyzed as exploratory explanatory variables in the mixed-effects models. No attempt was made to infer causal relationships between operational variables and measured sound levels. We hypothesized that environmental noise exposure would vary significantly across hospital functional zones and that departmental operational characteristics related to patient occupancy, staff activity, visitor activity, and environmental configuration would be associated with higher environmental sound levels.
Environmental noise measurements were obtained using a multistage hierarchical sampling framework consisting of repeated observations nested within hospital departments, functional zones, predefined sampling locations (entrance, middle, and end), standardized daytime observation periods, and technical measurement replicates. Standardized observation periods were defined as morning (09:00–11:00), midday (13:00–15:00), and afternoon (16:00–18:00), representing routine phases of daytime hospital activity while avoiding atypical transitional periods. Within each observation period, environmental noise was recorded using three consecutive 10-min technical replicates at each predefined sampling location, and the arithmetic mean of the three measurements was used for subsequent analyses. This hierarchical sampling design enabled characterization of both spatial and temporal variability in environmental noise while minimizing the influence of short-term fluctuations in routine hospital activity and improving the representativeness of measurements across different clinical environments.
The study is reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) recommendations for cross-sectional observational studies. The protocol and reporting were additionally prepared in accordance with the methodological and reproducibility requirements for observational research articles published in the Journal of Visualized Experiments (JoVE), including comprehensive documentation of sampling procedures, instrumentation, calibration records, measurement methodology, quality-control procedures, data processing, statistical analyses, and the availability of supporting source data.
Study Setting
The study was carried out at a large tertiary teaching hospital providing comprehensive medical and surgical services. Noise measurements were performed between March and August 2025 during routine weekday hospital operations. Data collection was restricted to weekdays to minimize variability associated with weekend staffing patterns and clinical activity. Data collection was conducted during daytime operational periods between 09:00 and 19:00, capturing representative acoustic conditions associated with routine patient care, staff movement, medical equipment operation, and visitor activity. Measurements were performed during three predefined operational periods (morning, midday, and afternoon) to account for temporal variation in routine hospital activities while maintaining a standardized daytime observation window. Measurements were obtained under routine clinical working conditions without interrupting or modifying patient care, staffing, or environmental controls, thereby reflecting real-world hospital acoustic conditions.
Departments and Sampling Framework
Fifteen inpatient departments representing a broad range of medical and surgical specialties were included in the study using a purposive sampling approach. Departments were selected to capture variability in clinical activity, patient volume, staffing patterns, departmental size, and spatial organization within the hospital environment.
The included departments were Trauma Orthopedics, Congenital Heart Disease Center, Vascular and Nerve Department I, Vascular and Nerve Department II, Intensive Care II Department, Parkinson’s Disease Department, Psychosomatic Medicine Department I, Otolaryngology–Cervicology Surgery, Knee Joint Department II, Hepatobiliary and Pancreatic Surgery, Gastroenterology Diagnosis and Treatment Department I, Gastroenterology Diagnosis and Treatment Department II, Urology Department, Breast and Thyroid Diagnosis and Treatment Center, and Pediatrics Department.
Within each department, environmental noise measurements were obtained from four standardized functional zones commonly evaluated in hospital acoustic surveys: the departmental ward, ward corridor, nurses’ station, and patient activity area. These locations were selected to represent distinct operational environments characterized by differing patterns of patient movement, staff communication, medical equipment use, visitor activity, and clinical workflow.
To improve spatial representativeness within each functional zone, three predefined sampling locations (entrance, middle, and end) were systematically evaluated. The entrance location was defined as the primary point of access to the functional zone, approximately 1–2 m inside the zone to avoid direct influence from adjacent areas. The middle location was positioned at the geometric center or principal activity area of the functional zone, representing routine operational conditions. The end location was established at the point farthest from the entrance while remaining within the same functional zone and accessible during routine clinical operations. Sampling locations were selected before data collection using a standardized protocol and maintained consistently across all departments. Where the physical configuration of a functional zone differed between departments, locations were selected according to these predefined spatial criteria rather than absolute distances to ensure methodological consistency. This multistage spatial sampling approach was adopted to better characterize acoustic variability within each operational area and to reduce the potential influence of localized sound sources associated with single-point measurements.
At each sampling location, environmental noise was recorded during three standardized observation periods (morning, midday, and afternoon). During each observation period, three consecutive technical replicate measurements were obtained using identical instrument settings. Consequently, the hierarchical sampling framework comprised repeated observations across 15 inpatient departments, four functional zones, three predefined sampling locations, three observation periods, and three technical replicates, yielding a total of 1,620 individual acoustic observations for analysis. This hierarchical sampling framework generated repeated observations across space and time, allowing characterization of both within-location and between-location variability in hospital environmental noise.
Departmental summary values reported in the manuscript were derived from these repeated measurements using predefined averaging procedures described below. The complete hierarchical dataset was retained for inferential statistical analyses as described in the Statistical Analysis section.
In addition to the inpatient departments, two representative public hospital areas—one indoor public activity zone and one outdoor public activity zone—were included to provide contextual environmental noise measurements outside direct patient-care environments. These public-area measurements were analyzed descriptively and were excluded from departmental statistical analyses, departmental summary statistics, and calculations of departmental measurement denominators.
Measurement Locations
Within each department, environmental noise measurements were obtained at four predefined functional zones commonly evaluated in hospital acoustic surveys: the departmental ward, ward corridor, nurses’ station, and patient activity area. These locations were selected to represent different patterns of clinical activity, patient movement, communication intensity, and equipment use within the hospital environment. The departmental ward represented general patient-care and bed areas; the ward corridor represented the primary circulation pathways for patients, staff, and equipment transport; the nurses’ station represented the central administrative and clinical coordination area; and the patient activity area represented designated spaces for patient movement, waiting, or interaction.
Within each functional zone, measurements were obtained at three standardized sampling locations (entrance, middle, and end), providing representative spatial coverage of each operational area. Sampling locations were selected to minimize the influence of localized sound sources while maintaining representative measurement positions within routine clinical environments. The microphone was positioned approximately 1.3 m above the floor and at least 1 m away from walls, large reflective surfaces, and major obstructions whenever feasible without interfering with routine clinical activities. The use of standardized functional zones and predefined sampling locations allowed consistent comparison of acoustic conditions across departments with differing clinical workflows and spatial layouts.
Noise Measurement Instrumentation
Environmental sound measurements were performed using a Class 1 precision integrating sound level meter compliant with IEC 61672-1:2013 and ANSI S1.4-2014 standards for precision environmental acoustic measurements. The instrument was configured using A-weighting [dB(A)] to approximate human auditory sensitivity and Slow (S) time weighting (1-second integration) to characterize routine environmental noise under operational hospital conditions. The primary acoustic parameter was the LAeq (dB[A]). In addition, the LAFmax and LCpeak were recorded during each measurement period to characterize transient and impulsive acoustic events occurring within the hospital environment. Measurements were conducted under routine hospital environmental conditions for observational acoustic assessment rather than formal architectural acoustic certification testing. Accordingly, the recorded acoustic parameters were interpreted as indicators of operational environmental noise exposure rather than engineering measures of building acoustic performance. Before and after each measurement session, the sound level meter was calibrated using a Class 1 acoustic calibrator generating 94 dB at 1 kHz. Calibration drift was maintained within ±0.5 dB throughout the study period. Measurements demonstrating post-calibration drift greater than ±0.5 dB were repeated following recalibration of the instrument. Calibration records were maintained throughout the study as part of the quality-control procedure.
Data Collection Procedure
Noise measurements were conducted during weekday daytime operational hours (09:00–19:00) to capture representative hospital acoustic conditions during routine clinical activity.
Within each functional zone, measurements were obtained sequentially at three predefined sampling locations (entrance, middle, and end). At each sampling location, observations were performed during three standardized operational periods (morning, midday, and afternoon). During each observation period, three consecutive technical replicate measurements were recorded using identical instrument settings. At each observation point, the microphone was positioned approximately 1.3 m above the floor and at least 1 m away from nearby walls or large reflective surfaces. Measurements were obtained during continuous 10-min recording intervals, and LAeq, LAFmax, and LCpeak were automatically calculated and stored by the sound level meter software for each recording interval.
During each observation period, operational characteristics of the measurement location, including staff count, visitor count, alarm events, and conversation events, were recorded using a standardized observation form. Departmental characteristics, including bed capacity, occupied beds, room area, and room volume, were obtained from hospital administrative records and verified before statistical analysis. To account for short-term temporal variability in hospital activity, measurements were repeated across the three predefined operational periods (morning, midday, and afternoon). For descriptive reporting, repeated technical replicate measurements were first averaged within each observation period, after which period-specific values were averaged to obtain representative values for each sampling location. Departmental and functional-zone summary statistics were subsequently derived using predefined averaging procedures, whereas the complete hierarchical dataset was retained for inferential statistical analyses. Ambient environmental conditions were monitored concurrently using a digital thermo-hygrometer to ensure relatively stable measurement conditions during data collection. Recorded environmental conditions ranged from 20°C–26°C for temperature and 40%–65% for relative humidity. One planned observation at the patient activity area of the Intensive Care II Department could not be completed because of temporary emergency-care restrictions during the measurement period. This observation was recorded as missing and excluded from subsequent analyses using a complete-case approach without data imputation.
Data Management and Quality Control
Measurement values were recorded manually at the time of acquisition and subsequently cross-verified against the digitally stored sound level meter recordings following each measurement session. All acoustic data were transferred to a secure electronic database and independently checked for transcription errors, missing values, duplicate records, and implausible observations before statistical analysis. Transient acoustic events unrelated to routine hospital operations, including temporary construction activity or external emergency sirens, were excluded when identified during monitoring. Two trained research assistants independently documented and verified all measurements to reduce observer-related recording errors. Any discrepancy greater than 1 dB between independently recorded values prompted re-verification of the original measurement and repeat assessment when necessary. Calibration records were reviewed for every measurement session, and only observations meeting the predefined calibration acceptance criterion (±0.5 dB drift) were retained for analysis.
Quality-control procedures also included verification of departmental identifiers, functional zones, sampling locations, observation periods, and technical replicate numbers to ensure consistency across the hierarchical dataset. Operational variables, including staff count, visitor count, alarm events, and conversation events, were cross-checked against standardized field-recording forms before database locking. One planned observation from the patient activity area of the Intensive Care II Department could not be completed because of temporary emergency-care restrictions and was recorded as missing. Because the proportion of missing data was minimal and unrelated to measurement quality, analyses were performed using a complete-case approach without data imputation. Because the study was designed as a practical environmental acoustic survey under real-world hospital operating conditions, formal engineering uncertainty analysis was beyond the scope of the investigation. Nevertheless, standardized instrumentation, routine calibration, repeated spatial and temporal measurements, technical replicate recordings, duplicate data verification, and predefined quality-control procedures were implemented to maximize measurement accuracy, consistency, and reproducibility.
Outcome Measures
The primary outcome of the study was the equivalent continuous environmental sound level (LAeq, dB[A]) measured across hospital departments and standardized functional zones. Secondary acoustic outcomes included the LAFmax and LCpeak, which were recorded during each measurement period to characterize transient and impulsive acoustic events within the hospital environment. Secondary outcomes included comparison of mean environmental noise levels between hospital departments and functional zones, identification of locations with relatively higher or lower acoustic exposure, assessment of the proportion of measurement points exceeding commonly referenced hospital environmental noise guidance values and standards, including WHO guidance values, EN ISO 16032, PN-B-02151, and the Chinese National Standard GB3096-2022, where applicable, and exploration of associations between environmental noise levels and objectively recorded operational characteristics, including departmental bed capacity, occupied beds, room area, room volume, staff count, visitor count, alarm events, and conversation events. Operational variables were evaluated as exploratory factors potentially associated with environmental noise exposure and were not interpreted as evidence of causal determinants of hospital acoustic conditions. The study was designed as an environmental acoustic assessment and did not include direct evaluation of patient clinical outcomes, occupational health outcomes, or physiological responses related to noise exposure. Accordingly, all findings should be interpreted as environmental acoustic observations rather than measures of patient safety, clinical effectiveness, or occupational health outcomes.
Statistical Analysis
All measurement data were compiled using Microsoft Excel and analyzed using IBM SPSS Statistics version 29.0 and R statistical software (version 4.3.3). Linear mixed-effects analyses were performed using the lme4, lmerTest, and emmeans packages. The primary analytical outcome was the LAeq. LAFmax and LCpeak were summarized descriptively and reported as secondary acoustic outcomes. Descriptive statistics, including the mean, standard deviation (SD), median, interquartile range (IQR), minimum, and maximum values, were calculated for hospital departments and functional zones. Continuous sound measurements are presented as mean ± SD where appropriate. Because environmental noise measurements were obtained using a hierarchical sampling framework, repeated observations were nested within sampling locations, functional zones, and hospital departments. Inferential statistical analyses were therefore performed using the complete hierarchical dataset rather than relying solely on department-level summary values.
The primary inferential analysis evaluated differences in LAeq across hospital functional zones using a linear mixed-effects model with hospital department specified as a random effect to account for clustering of repeated observations. Functional zone was included as the principal fixed effect, and estimated marginal means were compared using Tukey-adjusted pairwise comparisons when the overall fixed effect was statistically significant. Exploratory mixed-effects regression analyses were subsequently performed to evaluate associations between environmental noise and selected operational characteristics. To minimize model overfitting, the primary exploratory model included only operational variables with strong theoretical justification and acceptable multicollinearity, specifically departmental bed capacity and visitor count. Regression coefficients (β), 95% confidence intervals (CI), and corresponding P values were reported. A more comprehensive mixed-effects model incorporating occupied beds, room area, room volume, staff count, alarm events, and conversation events was performed as a sensitivity analysis and is presented in the Supplementary Material. All regression analyses were considered exploratory and were not interpreted as evidence of causal relationships.
Prior to model fitting, assumptions of approximate normality and homoscedasticity were evaluated using the Shapiro–Wilk test, inspection of residual plots, quantile–quantile plots, and residual-versus-fitted plots. Multicollinearity among explanatory variables was assessed using variance inflation factors (VIFs). Model fit was summarized using marginal and conditional coefficients of determination (R2), and intraclass correlation coefficients (ICCs) were calculated to quantify clustering by hospital department. A two-sided P value < 0.05 was considered statistically significant. Measured sound levels were additionally interpreted in relation to internationally referenced acoustic recommendations, including the WHO Environmental Noise Guidelines for the European Region (2018), EN ISO 16032 recommendations for building acoustics, PN-B-02151 healthcare acoustic standards, and the Chinese National Standard GB3096-2022 for hospital environmental noise. Because these reference documents differ in scope, intended application, and measurement methodology, comparisons were interpreted descriptively rather than as formal compliance assessments. The proportion of measurement points exceeding selected reference thresholds was calculated descriptively. To visualize spatial variability in environmental noise exposure across departments and hospital functional zones, heat maps, boxplots, and scatter plots with fitted regression lines were generated. Because the study involved repeated measurements within operational hospital environments, statistical analyses were interpreted as exploratory environmental comparisons rather than causal inference models. Missing measurements were handled using complete-case analysis without data imputation. The single missing observation from the Intensive Care II Department represented less than 1% of the complete dataset and was therefore considered unlikely to materially influence the overall findings.
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Study Characteristics and Measurement Framework
Environmental noise measurements were performed in 15 inpatient departments of a large tertiary teaching hospital between March and August 2025 using a standardized hierarchical sampling protocol (Figure 1). Within each department, four predefined functional zones (ward, patient activity area, corridor, and nurses’ station) were evaluated. Three fixed sampling locations were established within each f...
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This cross-sectional environmental acoustic assessment demonstrated that daytime environmental noise levels consistently exceeded commonly referenced international and national acoustic reference values across most hospital functional zones. Linear mixed-effects analyses identified functional zone as the principal factor associated with LAeq, with nurses’ stations exhibiting the highest adjusted sound levels, followed by corridors, patient activity areas, and wards. Comparisons with the WHO guidance values, EN ISO ...
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Competing Interests:
The authors declare that they have no competing interests.
None declared. No funding was received for this study.
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| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| Calibration Logbook | Self-prepared | N/A | Documentation of instrument calibration and verification |
| Class 1 Acoustic Calibrator | Hangzhou Aihua Instruments Co., Ltd. | AWA6221B | Calibration of the sound level meter (94 dB at 1 kHz) |
| Class 1 Integrating Sound Level Meter | Hangzhou Aihua Instruments Co., Ltd. | AWA6228+ | Primary instrument for environmental noise measurements (IEC 61672-1 compliant) |
| Data Collection Sheets | Self-prepared | N/A | Standardized recording of field observations and operational variables |
| Digital Thermo-Hygrometer | TESTO | 608-H1 | Monitoring ambient temperature and relative humidity during measurements |
| Hospital Floor Plans/Department Layout Maps | Institutional source | N/A | Identification of departmental measurement locations |
| IBM SPSS Statistics (Version 29.0) | IBM | N/A | Statistical analyses |
| Laptop Computer | Dell | Latitude Series | Data acquisition, storage, and statistical analysis |
| Measuring Tape | STANLEY | N/A | Verification of microphone placement distances |
| Microsoft Excel | Microsoft | Current version | Data management and preliminary data processing |
| Personal Protective Equipment | Institutional supply | N/A | Compliance with hospital infection-control requirements |
| R Statistical Software (Version 4.3.3) | R Foundation for Statistical Computing | N/A | Statistical computing environment |
| R packages (emmeans, lme4, lmerTest) | Comprehensive R Archive Network (CRAN) | Current versions | Linear mixed-effects modeling and estimated marginal means |
| Rechargeable Battery Pack | Hangzhou Aihua Instruments Co., Ltd. | Manufacturer supplied | Power supply for the AWA6228+ Class 1 Integrating Sound Level Meter |
| Sound Level Meter Software | Hangzhou Aihua Instruments Co., Ltd. | Included with instrument | Instrument control, data acquisition, and export of acoustic measurements |
| Tripod Stand | Generic laboratory supplier | N/A | Stable positioning of the sound level meter microphone |
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